In this paper, we propose and study a simple, inexpensive yet effective data augmentation method, which we call Noisy Feature Mixup (NFM).
This method combines mixup and noise injection, thereby inheriting the benefits of both methods, and can be seen as a generalization of input mixup [1]} and manifold mixup [2]}.
When compared to noise injection and mixup, NFM imposes regularization on the largest natural region surrounding the dataset (see Fig. REF ), which may help improve robustness and generalization when predicting on out of distribution data.
Conveniently, NFM can be implemented on top of manifold mixup, introducing minimal computation overhead.
